Search Results for author:
Found 50 papers, 31 papers with code
AI-driven 3D Spatial Transcriptomics
A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications.
Accelerating Data Processing and Benchmarking of AI Models for Pathology
Advances in foundation modeling have reshaped computational pathology.
Molecular-driven Foundation Model for Oncologic Pathology
Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks.
Multimodal Whole Slide Foundation Model for Pathology
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL).
Multistain Pretraining for Slide Representation Learning in Pathology
Existing approaches for slide representation learning extend the principles of SSL from small images (e. g., 224 x 224 patches) to entire slides, usually by aligning two different augmentations (or views) of the slide.
Composable Interventions for Language Models
Test-time interventions for language models can enhance factual accuracy, mitigate harmful outputs, and improve model efficiency without costly retraining.
Multimodal Prototyping for cancer survival prediction
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification.
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity.
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments
A potential early route towards clinical adoption for 3D pathology is to rely on pathologists for final diagnosis based on viewing familiar 2D H&E-like image sections from the 3D datasets.
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL).
Transcriptomics-guided Slide Representation Learning in Computational Pathology
Across three independent test datasets consisting of 1, 265 breast WSIs, 1, 946 lung WSIs, and 4, 584 liver WSIs, Tangle shows significantly better few-shot performance compared to supervised and SSL baselines.
Artificial Intelligence for Digital and Computational Pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology.
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4.
A General-Purpose Self-Supervised Model for Computational Pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology.
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D).
Towards a Visual-Language Foundation Model for Computational Pathology
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts.
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels.
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
We propose fusing both modalities using a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens.
Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different modalities.
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations.
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling
Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment.
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e. g. - 256x256, 384384).
Algorithm Fairness in AI for Medicine and Healthcare
In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care.
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied.
Fast and Scalable Image Search For Histology
Similar pathology image search offers the opportunity to comb through large historical repositories of gigapixel WSIs to identify cases with similar morphological features and can be particularly useful for diagnosing rare diseases, identifying similar cases for predicting prognosis, treatment outcomes, and potential clinical trial success.
Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival.
Deep Learning-based Frozen Section to FFPE Translation
In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes.
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs).
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy.
VR-Caps: A Virtual Environment for Capsule Endoscopy
Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions.
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
The codes and the link for the dataset are publicly available at https://github. com/CapsuleEndoscope/EndoSLAM.
Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary
Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined.
Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics.
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data.
Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands.
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL).
SLAM Endoscopy enhanced by adversarial depth prediction
Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct depth-sensing.
GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images
In human gastrointestinal specimens, GANPOP estimates both reduced scattering and absorption coefficients at 660 nm from a single 0. 2/mm spatial frequency illumination image with 58% higher accuracy than SSOP.
Structured Prediction using cGANs with Fusion Discriminator
We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct structured prediction tasks, including image synthesis, semantic segmentation, and depth estimation.
Multimodal Densenet
In this work, we propose Multimodal DenseNet, a novel architecture for fusing multimodal data.
DeepLSR: a deep learning approach for laser speckle reduction
In images of gastrointestinal tissues, DeepLSR reduces laser speckle noise by 6. 4 dB, compared to a 2. 9 dB reduction from optimized non-local means processing, a 3. 0 dB reduction from BM3D, and a 3. 7 dB reduction from an optical speckle reducer utilizing an oscillating diffuser.
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
However, CNNs require large amounts of labeled histopathology data.
Rethinking Monocular Depth Estimation with Adversarial Training
Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function.
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images
Our experiments demonstrate that: (a) Convolutional Neural Networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model.
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
We propose an alternative framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and hypothesize that clinically-relevant features can be preserved via self-regularization.
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy
We show that the estimated depth maps can be used for reconstructing the topography of the mucosa from conventional colonoscopy images.
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions.
2D Discrete Fourier Transform with Simultaneous Edge Artifact Removal for Real-Time Applications
These artifacts can have critical consequences if the DFTs are being used for further processing.
Graph Based Sinogram Denoising for Tomographic Reconstructions
This makes the sinogram an ideal candidate for graph based denoising since it generally has a piecewise smooth structure.
